{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BioModels service from BioServices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "from bioservices import *\n",
    "%pylab inline\n",
    "matplotlib.rcParams['figure.dpi'] = 2 * matplotlib.rcParams['figure.dpi'] \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO    [bioservices:BioModels]:  Initialising BioModels service (REST)\n"
     ]
    }
   ],
   "source": [
    "s = BioModels()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us look at a model that represents signalling pathways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomodel = s.get_model(\"MODEL1305240000\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and save it in a local file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO    [bioservices:BioModels]:  Saving MODEL1305240000_urn.xml\n"
     ]
    }
   ],
   "source": [
    "biomodel = s.get_model_download(\"MODEL1305240000\", \"MODEL1305240000_urn.xml\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Here below is a piece of code not provided within bioservices that illustrates what could be done with the output of the biomodels downloaded here above</b> Requires the package cellnopt.core (version 0.6) available on pypi. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cno import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIF object\n",
       "- 33 reactions.\n",
       "- 30 species."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SIF(); \n",
    "model.read_sbmlqual(\"MODEL1305240000_urn.xml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = cnodata(\"MD-ToyPB.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING [root]:  The signals ap1 was found in the MIDAS file but is not present in the model. Adding that node to your model\n",
      "WARNING [root]:  The signals erk was found in the MIDAS file but is not present in the model. Adding that node to your model\n",
      "WARNING [root]:  The signals gsk3 was found in the MIDAS file but is not present in the model. Adding that node to your model\n",
      "WARNING [root]:  The signals nfkb was found in the MIDAS file but is not present in the model. Adding that node to your model\n",
      "WARNING [root]:  The signals p38 was found in the MIDAS file but is not present in the model. Adding that node to your model\n",
      "WARNING [root]:  The signals raf1 was found in the MIDAS file but is not present in the model. Adding that node to your model\n"
     ]
    }
   ],
   "source": [
    "c = CNOGraph(model, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a02c983c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "For more information, please see <a href=\"http://pythonhosted.org//bioservices/references.html?bioservices.biomodels.BioModels#module-bioservices.biomodels\" target=\"_blank\">bioservices.biomodels module documentation.</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
